Search Results (30)

As the global energy transition progresses, green electricity, which is crucial for low-carbon systems, has gained attention. However, the lack of effective market linkages hinders a full understanding of the price transmission effects across green markets. This study uses the Vector Autoregression (VAR) model and Granger causality tests to analyze the price transmission and lag effects between the carbon, green certificate, and China Certified Emission Reduction (CCER) Markets. The findings reveal complex price linkages, offering theoretical insights and policy recommendations for optimizing green electricity markets and environmental rights trading. Full article

This study proposes a full-cost electricity pricing model (M3) based on power flow tracing, addressing limitations in traditional nodal pricing and postage stamp methods. M3 dynamically allocates fixed transmission costs based on actual grid utilization, improving fairness, price signal accuracy, and congestion management. The model achieves fast convergence within 20 iterations across tested networks. Sensitivity analysis confirms that fuel costs and load variations significantly impact pricing, making M3 more adaptive and responsive. A regression-based forecasting model further enhances price predictability. The dual IEEE 118-bus case study validates M3’s feasibility in inter-provincial electricity markets, demonstrating its effectiveness for real-time pricing and investment planning. Full article

Climate change manifests itself in rising temperatures across the continent and affects the El Niño–Southern Oscillation (ENSO) by changing sea surface temperatures and atmospheric circulation. This affects precipitation and temperature patterns, with South Africa normally experiencing drier conditions during El Niño events. These weather anomalies influence crop yields and food prices. Spatial price transmission indicates the extent to which prices of agricultural goods are linked across different geographical areas and how quickly price signals from one area are passed on to another. Although numerous studies explore spatial price transmission between various countries, there is a gap in the literature on price transmission between the US and South African maize markets during ENSO events. Therefore, we investigate how ENSO-related events impacted maize price transmission between the Chicago Mercantile Exchange and the Johannesburg Stock Exchange from 1997 to 2024. The empirical analysis starts with a correlation analysis, followed by tests for cointegration and error correction models. The results confirm the dominating impact of US maize prices on South African prices, but also how this relationship changes based on the nature of the ENSO event. There is some indication of lower levels of integration and higher levels of price diversion during El Niño periods. Full article

The asymmetric effects of global and national factors on agricultural production negatively affect the sustainability of agriculture in Turkey. This study seeks to explore those impacts on wheat prices by focusing on key input prices such as diesel, fertilizers, and substitute barley prices and wheat production. Unlike studies that use crude oil prices as agricultural input parameters, this study aims to address the lack of behavioural variables in time series studies by considering diesel and fertilizer prices. The Vector Autoregressive (VAR) model analysis examines the effect of barley prices as a substitute for wheat, while the Granger causality analysis is conducted to assess the causal relationships between variables. Additionally, unlike previous studies that primarily focus on causality between variables or the effects of lagged values, this study investigates the dual effects of explanatory variables. Furthermore, impulse response functions are utilized to analyse the dynamic interactions among the variables and to identify symmetric and asymmetric relationships. Granger causality analysis indicates that wheat production in Türkiye is influenced by wheat prices; however, production does not impact prices. Wheat prices are not market-driven, and price interventions aim to ensure agricultural sustainability. The absence of causality between the wheat production amount and its price emerged bilaterally as barley price/wheat production/barley price. An analysis of wheat price responses to shocks in fertilizer and diesel prices reveals an asymmetric pattern. Wheat prices reacted more strongly to negative shocks, while their response to positive shocks was more moderate. These findings indicate the existence of asymmetric relationships between wheat prices and these two agricultural inputs, underscoring the asymmetric nature of price transmission in agricultural markets. They also highlight the policy requirements associated with ensuring food price stability and sustainable agricultural practices as well as a crucial lesson: policymakers in developing countries should prioritize structural reforms over interventionist policies that distort market signals. Full article

This paper examines the transformative effects of decarbonization on electricity market design, emphasizing the challenges and opportunities posed by the rapid integration of renewable energy sources such as wind and solar. It analyzes the evolution of key wholesale market segments—including day-ahead, real-time, capacity, long-term purchase agreements, ancillary services, and transmission markets—highlighting their critical roles in managing the variability of renewable energy generation through efficient price signals and resource coordination. Variable renewable energy integration introduces significant operational challenges, including overgeneration risks, ramping capacity demands, forecast inaccuracies, and transmission constraints. Addressing these issues requires enhanced market flexibility, dynamic pricing mechanisms, and advanced real-time balancing strategies. This paper assesses these challenges, offering strategies to align generation with demand and optimize market outcomes. As electricity systems evolve, legacy market structures must adapt to incorporate carbon-free resources while maintaining grid reliability and economic sustainability. By exploring case studies such as Chile and California, this paper demonstrates the importance of targeted innovations in market design, regulatory frameworks, and operational technologies. It advocates for a holistic approach to ensure a reliable, affordable, and equitable transition to a decarbonized energy future. Full article

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load. Full article

This study delves into the dynamics of price linkages and transaction costs in agricultural markets, emphasizing the sustainability of food supply chains. By exploring vertical and horizontal price linkages in agro-farming value chains of a developing country, it addresses the efficiency of market information transmission and the capacity for arbitrage among chain participants. The methodological core of the research involves analyzing price linkages in Chilean horticultural wholesale markets, focusing on key vegetables such as, onions, lettuce, maize, and tomatoes. This analysis is underpinned by a novel approach that models and estimates time-dependent, conditional threshold bands, extending the traditional cointegration models. This method allows a more nuanced understanding of how agricultural market linkages evolve over time, enhancing our comprehension of price transmission behavior and market integration. The results reveal significant non-linear relationships between fuel prices and vegetable prices, particularly in central Chilean regions. This finding challenges the traditional linear perspective, suggesting that factors such as storage capacity and arbitrage behavior can influence price signal transmission. Such insights are crucial for stakeholders in the agribusiness value chain, offering a deeper understanding of market dynamics and aiding in the development of more sustainable and efficient market strategies. This research contributes significantly to the field of agricultural economics by providing a more robust framework for analyzing market behaviors and transaction costs in the context of sustainability and value chains. Its findings have profound implications for both theory and practice, informing policy-making and strategic decision-making in the agribusiness sector. Full article

Although there are many methods that can be used to obtain customer preferences for new energy vehicles, most studies generally overlook the fact that customer preferences are private information. The purpose of this study is to investigate the transmission mechanism of customer preferences by taking into account situations in which customers lie. Through a signaling game model, this study analyzed the transmission mechanism of customer preference information for the center control touch screen of new energy vehicles based on separation equilibrium. The results show that when inequality (1) remains, such an equilibrium forms: the customers send the real preference signal, the manufacturer then adopts a new sample consistent with the received signal and prices the product accordingly, and, finally, the customers pay for the new NEV. When inequality (2) remains, the following equilibrium forms: customers signal the opposite of their private preference, the manufacturer then adopts a new sample opposite to the received signal, and, finally, customers pay for the new NEV. Full article

Distributed resources at a grid’s end cannot upload operational power data to local centers due to data transmission and privacy issues. This leaves the centers with incomplete information, thus impacting decision making. This paper presents a virtual aggregation-based model for such scenarios. We define four virtual aggregate types based on resource response characteristics. Using characteristic coefficients, we identify these aggregates’ categories and proportions from bus power. To address blind source separation in single-channel power signals, we apply the Ensemble Empirical Mode Decomposition-Fast Independent Component Analysis (EEMD-FastICA) method. This helps extract and analyze bus power, thereby deriving power curves for different aggregates. Moreover, we use a graph convolutional network to explore how factors like date, time, weather, and pricing intertwine with aggregate power. We develop a predictive model with an advanced SpatioTemporal Graph Convolutional Network (STGCN), thus facilitating proactive power forecasting for virtual aggregates. Case studies show our method’s efficacy in extracting power curves under limited information, with the STGCN ensuring accurate, forward-looking predictions. Full article

With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation. Full article

Recently, intelligent reflecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of air–ground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reflecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme based on successive convex approximation and fractional programming (FP) called the maximal signal-to-noise ratio (SNR)-FP (Max-SNR-FP) is proposed. Given its high complexity, we propose a low-complexity maximal SNR-equal amplitude reflecting (EAR) (Max-SNR-EAR) scheme based on the maximal signal-to-leakage-noise ratio method, and the criteria of phase alignment and EAR. Given that the active and passive IRS phase shift matrices of both schemes are optimized separately, to investigate the effect of jointly optimizing them to improve the achievable rate, a maximal SNR majorization-minimization (MM) (Max-SNR-MM) scheme using the MM criterion to design the IRS PSM is proposed. Simulation results show that the rates harvested by the three proposed methods were slightly lower than those of the active IRS with higher power consumption, which were 35% higher than those of no IRS and random phase IRS, while passive IRS achieved only about a 17% rate gain over the latter. Moreover, compared with the Max-SNR-FP, the proposed Max-SNR-EAR and Max-SNR-MM methods caused obvious complexity degradation at the price of slight performance loss. Full article

With special emphasis in recent years, an increase has been verified not only in demand but also in the price of electricity, arising the need to develop more reliable and efficient electrical energy conversion systems. In this context, emerges the utilization of the modular multilevel converter (MMC) based on submodules. The key to the MMC is modularity, which allows the converter to reach higher performance levels, improving the voltage and current output signals of the converter, in a compact solution. The modularity concept allows the increase of the operation voltage using submodules in series, and the increase of the operating current using submodules in parallel. Additionally, in the event of a submodule malfunction, the converter can be reconfigured and continue the operation, albeit at a lower power level. Due to its versatility, the MMC can be used in a variety of applications, such as HVDC power transmission systems, solid-state transformers, renewable energy interfaces, and more recently, railway power systems. In this context, this paper focuses on the development and experimental validation of a single-phase MMC based on the use of half-bridge submodules applied to a railway static converter, where the main focus lies on the AC side control. The control algorithms are fully described for a single-phase MMC reduced-scale prototype implemented (500 W, 230 V–50 Hz, 200 VDC), connecting two submodules in series in the upper arm, two submodules also in series in the lower arm, the respective driver and command circuits, sensing and signal conditioning circuits, as well as a digital control platform recurring to the DSP TMS320F28379D. Experimental results were obtained to validate each submodule individually, and, later, to verify the operation of the MMC with the set of four submodules. Full article

The increasing penetration of renewable and distributed resources signals a global boom in energy transition, but traditional grid utilities have yet to share in much of the triumph at the current stage. Higher grid management costs, lower electricity prices, fewer customers, and other challenges have emerged along the path toward renewable energy, but many more opportunities await to be seized. Most importantly, there are insufficient studies on how grid utilities can thrive within the hydrogen economy. Through a case study on the State Grid Corporation of China, we identify the strengths, weaknesses, opportunities, and threats (SWOT) of grid utilities within the hydrogen economy. Based on these factors, we recommend that grids integrate hydrogen into the energy-as-a-service model and deliver it to industrial customers who are under decarbonization pressure. We also recommend that grid utilities fund a joint venture with pipeline companies to optimize electricity and hydrogen transmissions simultaneously. Full article

The national carbon emission trading mechanism is an important policy tool for the Chinese government to control and reduce greenhouse gas emissions by using the market mechanism. The Beijing–Tianjin–Hebei power market is the focus of energy conservation and consumption reduction in China. Problems have already existed in the synergic development of the Beijing–Tianjin–Hebei power market and carbon trading market. In this article, the development status of the Beijing–Tianjin–Hebei power market is analyzed and the coupling mechanism between the carbon market and power market is combed out to build a synergism model of the carbon market and the Beijing–Tianjin–Hebei power market based on the system dynamics. From the research results, firstly, the Beijing–Tianjin–Hebei power market comes with a high energy consumption intensity and a high proportion of carbon emissions. The coupling of carbon market and power market forces the power industry to reduce carbon emissions through the effective transmission of carbon costs to power prices. Secondly, carbon price shows an upward trend in the context of the current policy scenario, which can give play to the role of price signal in the future. The revenue of thermal power plants, which are the carbon emission right sellers, with new technologies, has increased significantly, while the revenue of carbon emission right buyers, which are the manufacturers of undeveloped units, has increased less. Finally, the technical progress of thermal power plants, the introduction of auction mechanism, the increase in initial carbon price settings and the direct transmission of carbon costs are all factors that promote the effectiveness of carbon trading policy tools in the Beijing–Tianjin–Hebei power market. This study provides theoretical guidance for the synergic development of the “power-carbon” market. Full article

Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient S₁₁ and transmission coefficient S₂₁. This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network. Full article